This invention relates to a method for displaying a graphic containing contours on a display unit.
The present invention relates to the area of portable information and communication technology devices. Dictated by the relatively small size and the associated low pixel and color resolution of a display unit on a portable device graphic displays such as conventional images or special graphics must be edited for display. This editing is also necessary for the reduced display of large maps on high-resolution screens or a display with few colors for the purposes of image compression.
In Document WO 00/46748 A1 a method for creating a color descriptor of an image is published: This method includes the following steps:    a) Determining the color vectors of a specified image;    b) Classification of the color vectors in order to determine the dominant color and the interrelationships;    c) Display of the dominant color's and their relationships to each other as a color descriptor of the specified image.
The color descriptor of an image mentioned above includes typical characteristics of an image. The method in accordance with the document WO 00/46748 A1 is used in object-based image processing systems to allow a simpler search and faster location of a specific content or pattern.
As in the document mentioned above the images or graphics are mostly present in an n·m pixel format. Each pixel in this case is assigned a specific position within a grid and a specific color. Graphics such as topographical or geographical maps are mostly originally present in a vector representation. Such maps will however be previously converted into a pixel format of the type mentioned above for publication or for output at a display unit.
Editing the display requires a reduction in the number of pixels. This is normally done by a selection method, referred to technically as <<subsampling>>. In this case methods are used such as formation of averages or a subsampling of a specific pixel from a matrix of for example 4×4 pixels. For example the pixel located in the top left corner of this 4×4-matrix can be used here. These procedures are entirely suitable for images which do not feature any specific contours. The term <<specific contours>> includes the fact that these contours are assigned a defined semantic such as for example in the area of topography in accordance with the representation shown below in Table 1.
TABLE 1Semantic of the colors with regard to the contoursContourSemanticBlue lineRiver, streamBlue line which delimits aShore of a lakelight blue surface.Thin black lineCo-ordinate gridThin brown LineHeight contourGreen line which delimits aWoodland edgelight green surface.
The disadvantage of the above-mentioned methods of displaying such graphics is that, because of the absence of these contours the readability is significantly adversely affected. The term contour used above and its significance is in no way restricted here to cartography but can for example also be applied to other graphics such as for example a graph curve or a temperature curve within a Cartesian co-ordinate system.
The negative effects in the presentation of a graphic in a pixel display such as for example anti-aliasing can also be rectified by what is known as <<super sampling>>. <<Super sampling>> means that the pixels are initially edited in a memory. In this case this memory is virtually assigned a higher resolution than the actual resolution on the display unit. The disadvantage of this method (also called: <<super sampling method of anti-aliasing>>) is the high memory requirement and the associated high computing power. To ameliorate the problem of a high memory requirement somewhat it is proposed in EP 1 056 047 A1 to remedy this by a weighted decomposition into three basic colors and a subsequent linear combination of the colors for each pixel. However this does not display colors with a specific significance any better since even at the high resolution the individual elements are present as <<areas>> and not as contours.
The method described in WO 00/46748 A1 is therefore not suitable for contour-containing reduction of an image because it is to be applied above all to a large monochrome regions: Fine contours especially get lost in filtering as noisy pixels>>, see WO 00/46748 A1, Page 4 for more information.